Previous research has addressed the concepts of inhabiting a humanoid robot through an intuitive user interface. In (Trahanias, Argyros, et al., 2000), the authors' presented a system, “Tourbot”, which enabled users to remotely explore a museum through a robotic avatar. Instead of allowing the user to directly control the robot's motion, users were given a set of high level commands such as what exhibit to view, promoting ease of use. Additionally, (Marn, Sanz, et al., 2005) created a highly general control system for manipulating a robot arm that allowed both high level voice commands and low level remote programming through Java. Another project (Bell, Shenoy, et al., 2008) explored a noninvasive brain computer interface using electroencephalography (EEG). Because of the large amount of noise associated with EEG, the authors focused on robot control through simple high level commands. This work focused on indirect control, in which individual joints are not mapped onto a robot.
In other research, (Tachi, Komoriya, et al. 2003) presented a “telexistence cockpit” capable of remotely controlling a robot. The user interface consisted of two “master arms,” a head mounted display (HMD), and a motion base located in the cockpit. A user would manipulate the master arms to control the slave robot arms. Additionally, the motion base was used to communicate robot translation and the HMD adjusted the view perspective.
Other researchers have explored how human motion can be realistically mapped onto a robot. In (Pollard, Hodgins, et al., 2002), the authors prerecord human motion and map the resulting animation onto a robot. Because a human has many more degrees of freedom than the robot, the human body was divided into segments and the overall rotations from these segments were mapped onto the robot. Furthermore, the authors scaled the original animation to fit inside the range of motion of the robot's joints.
Another related paper (Nakaoka, Nakazawa, et al., 2003) addressed the more specific problem of mapping a human dance onto a robot surrogate. The researchers motion captured a human dance performance, and the resulting animation was then divided into a set of important postures for a robot to assume. The authors were interested in full body motion, and as a result, had to modify the captured data to ensure the robot would maintain its balance.
The previous approaches are limited by the quality of the motion capture software, and in some cases may not replicate the motion as desired. In (Calinon and Billard, 2007), the authors presented a method of teaching a robot a gesture through a two phase process. In the first phrase, a human coach, wearing several motion sensors, performed a gesture while the robot observed. In the second phase, the coach was allowed to grab and move the robot arms to refine the gesture.